International Journal of Advancements in Research & Technology, Volume 2, Issue 12, December-2013
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EFFECTS OF THE ADOPTION OF EXTENSION TECHNOLOGIES ON THE
TECHNICAL EFFICIENCY OF MAIZE FARMERS IN OYO STATE
Olapade-Ogunwole F Adedeji I.A and Ogunniyi L.T
Ladoke Akintola University Of Technology Ogbomoso Oyo State Nigeria
ABSTRACT
This research work broadly examined the effects of adoption of extension technologies on the
technical efficiency of maize farmers in Oyo state, Nigeria. The study is specific objectives
are to examine the socio economic characteristic of the maize farmers, investigate the
different maize extension technologies adopted by the respondents, determine the technical
efficiency of the maize farmers and identify the constraints faced by the maize farmers during
the adoption of maize extension technologies in the study area. The study employed the use
of cross -sectional data from farm business survey conducted on a sample of 260 farmers
from different local government areas of the study. The data were collected with the aid of
structured questionnaire and were later analyzed.
The study employed the following analytical tools in order to analyze the data
collected from the field. Descriptive statistics such as frequency distribution, percentages
were used to describe the socio economic characteristics of maize farmers while econometric
analytical tools involved such as regression analyses used to test for the first hypothesis; logit
model was the used to test the second hypothesis while stochastic frontier production
function analysis was used to analyses the technical efficiencies of maize farmers as well as
the determinant of their technical efficiencies.
Among the maize farmers, the variable that were significant includes labour (at 5%)
fertilizer quantity used (10%) seed quantity used (1%) and herbicides quantity used (1%). By
implication the above findings revealed that the major productive inputs that greatly impacted
on the maize roping of the maize farmers’ enterprise were the aforementioned significant
variables while the herbicides quantity used existed as the most important input that impact
on maize output of the respondents
Regressions analysis also shows that there is significant relationship between the
socio-economics characteristics of the respondents and the maize output, therefore the
formulated first hypothesis was also rejected and the alternative hypothesis was accepted.
The mean technical efficiency of the maize farmers showed that the maize farmers
were technically efficiency based on the stochastic frontier results, therefore the formulated
second hypothesis was also rejected.
KEYWORD; Effect, Adoption, Extension Technologies, Maize Farmers.
INTRODUCTION
Many researchers and policymakers have focused on the impact of adoption of new
technologies in increasing farm productivity and income (Hayami and Ruttan, 1985).
However, during the last two decades, major technological gains stemming from the green
revolution seem to have been largely exhausted across the developing world. This suggests
that attention to productivity gains arising from a more efficient use of existing technology is
justified (Bravo-Ureta and Pinheiro, 1997). Inefficiency in production means that output can
be increased without additional conventional inputs and new technology. Therefore,
empirical measures are necessary to determine the gains that could be obtained by improving
efficiency in agricultural production with a given technology. An important policy
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implication stemming from significant levels of inefficiency is that it might be cost effective
to achieve short-run increases in farm output, and thus income, by concentrating on
improving efficiency rather than on the introduction of new technologies (Shapiro and
Müller, 1977).
Agriculture is the mainstay of the Nigeria economy and is characterized by mixed
farming system. Expected increases in agriculture requires increase in agricultural
productivity. Agricultural productivity very much depends on the efficiency of the production
process. Policies designed to educate people through proper agricultural extension services
could have a great impact in increasing the level of efficiency and hence agricultural
productivity.
Agricultural growth in Nigeria is increasingly recognized as central to sustained
improvement in economic development of the nation and food security of the population.
Agriculture has many ascribed roles to perform in the course of the country’s economic
development. Critical among these roles are those of providing adequate food for an
increasing population, supply of adequate raw materials to a growing industrial sector and
foreign exchange earnings.
Technical efficiency measurement has been the concern of researchers with an aim to
investigate the technical efficiency levels of farmers engaged in agricultural activities.
Empirical studies in developing countries suggest that farmers are unable to utilize maximum
potentiality of technology due to their management capacity.
The government uses extension as a support service as well as a policy instrument for
influencing farmer’s behaviour to achieve its policy goals. In designing an extension system,
contact with farmers, their active participation and the determination of farmers' need for
technology are critical (Basnyat, 1995).
.
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Improved Agricultural Technology
The Nigerian policy makers generally agreed that development of agricultural system
depends on the rate at which we achieve the intensification of agricultural activities, Falusi
(1973), Falusi and Adubifa (1975), Umeh (1990, 1997) Antle (2003). One way of doing this
is through the adoption of improved technologies. Olayide (1980) defined technology as the
systematic application of collective human rationality to the solution of problems by asserting
control over nature and human processes of all kinds. Technological development has also
been defined as the technological change or progress that culminates in an increase in
productivity or efficiency. Technological development occurs in step-by-step direction,
starting with invention or applied research, testing of the feasibility using small-scale models
innovation or actual use of the model and the diffusion of the change.
The term “agricultural technology” connotes different things depending on the
inclination of the contributor. It can be defined to suit individual and or organization’s aims
and objectives. Nelson and Phelps (2006) defined technology as all improved methods,
techniques, materials, tools etc. aimed at agricultural modernization. It includes such things
as new implements, improved tools, improved seeds, fertilizer, insecticides, pesticides,
storage facilities, irrigation facilities etc. Tweeten (2007), also defined technology as those
forces that increase farm output with a given dollar volume of production inputs (farmland,
labour, and purchased inputs including new and improved inputs and changes in farm
management practices, specialization, and institutions serving agriculture.
The current novel thing in the developing countries is to indulge in technological
package recommendation. By this, an innovation consists of a set of specific packages. Here,
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technical recommendations are presented as a set of interdependent prescriptions to be
adopted in conjunction with each other or along a particular scenario. When a new hybrid
seed is being recommended to the farmers, there are specific recommendations on such
activities as land selection, preparation, sowing, agronomic practices including weeding,
fertilization etc. to complement. This is largely the approach adopted in the Agricultural
Development Programme in Nigeria and is based on the premise that the effectiveness of one
factor or practice frequently would be enhanced because of its use in conjunction with a
complex of other improved practices. For example, a study of United States agriculture
revealed that the hybrid corn when first introduced in the south eastern United States caused
relatively small yield increase per acre, and therefore, hybrid varieties did not add much to
farm incomes. However when improved hybrid corn was combined with more fertilizer,
closer spacing of plants and good tillage practices, the hybrids yielded highly satisfactory
results. In essence at technological package is a combination of biological, chemical and
mechanical technologies.
Available technologies can be broadly classified based on the sources, type and their
characteristics. Technology can take two forms. First new products can be introduced, second
new processes can be developed which give more output per unit of resource employed.
Benefits from technology arise principally in two forms; either the new state of art saves
resources or it provides a larger output from a given resource commitment. Stoneman (2003)
further classified technology as either embodied or disembodied. It defined disembodied
technological change as a state where “independent of may changes in the factor inputs, the
isoquant contours of the production functions shift towards the origin as time passes, while
embodied technological change comprises improvements that are only associated with
investment in new equipment and skills, with old equipment left unenhanced. The new
technology is built into or embodied in new capital equipment or newly trained or retrained
labour.
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Statement of the Problems
Extension has diverse definitions but can be summarized as a field where agricultural
professionals play a role in identifying, adapting and sharing technology that is appropriate to
the needs of individual farmers within diverse agro-ecological and socioeconomic contexts
(Landon and Powell, 1996). Many factors contribute towards the development of agriculture,
including extension as an institutional input. According to a worldwide survey conducted by
the FAO in 1988–9, about 81% of extension work around the world is carried out through a
ministry or department of agriculture (Umali and Schwartz, 1994).
Agricultural extension service in Nigeria is said to be ineffective and inefficient in
transferring the technologies to the farmers. Sacay (1987) stated that the level of adoption of
technology by the Nigeria farmers was low. For one reason or another, they have not taken
advantage of modern technology. The force of non extension variables could be far greater
than the so-called extension variables affecting food production and income in the
agricultural rural sector. Implementation of more than one extension approach in the same
district and village, lack of clear and timely policy guidelines, ignorance of non awareness to
the policies laid down in the development plan of the country and no commitment to
implement the strategies and directives given in the development plan are examples of the
lack of national policy guidelines in Nigeria (Basnyat, 1995).
In addition, there is growing uncertainty about what role extension is supposed to play
in the development process. There is now a much-reduced emphasis on uniform messages
(such as those provided by the T&V system). The need to involve farmers more in the
extension process itself has been recognized for some time and a number of participatory and
facilitation approaches have been developed (Roling, 1995; Coldevin, 2000). Now, however,
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serious reservations are being expressed about the performance and capability of this sector,
placing the future of the public extension system in doubt. Furthermore, large numbers of
farmers remain outside the ambit of extension providers (Prinsley et al., 1994).
Khairo and Battese (2005) found that agricultural extension advice, formal schooling, offfarm income and the ages of maize farmers were important factors affecting the technical
inefficiencies of maize farmers within the new agriculture extension program in the Harari
Region of Ethiopia. Therefore, they concluded that agricultural policy makers need to look
for alternative means of strengthening the social and economic basis of maize farmers in
order to address resource constraints and low productivity in maize production.
Extension for sustainable agriculture systems must therefore emphasize helping
individual farmers critically assess their situations and promote local cooperation and
coordination of common resources. In order to move from a teaching paradigm towards a
learning paradigm, highly participatory interaction and knowledge sharing among all actors is
critical for extension institutions both in applied extension programs and at teaching
institutions (Roling and Pretty, 1997). This is attributed to a number of factors including
poorly motivated staff, a preponderance of non extension duties, inadequate operational
funds, lack of relevant technology, topdown planning, centralized management, and a general
absence of accountability in the public sector (Antholt, 1994). Overall, public extension
services have consistently failed to deal with the site-specific needs and problems of the
farmers (Ahmad, 1999). Production efficiency has two components: technical and allocative
efficiency. Technical efficiency is the extent to which the maximum possible output is
achieved from a given combination of inputs. On the other hand, a producer is said to be
allocatively efficient if production occurs in a subset of economic region of the production
possibilities set that satisfies the producer's behavioral objective (Ellis, 1988). Farrell (1957)
distinguishes between technical and allocative efficiency in production through the use of a
'frontier' production function. Technical Efficiency is the ability to produce a given level of
output with a minimum quantity of inputs under certain technology. Allocative efficiency
refers to the ability of choosing optimal input levels for given factor prices. Overall
productive efficiency is the product of technical and allocative efficiency.
Thus, if a farmer has achieved both technically and allocatively efficient levels of
production, then the farmer is economically efficient. In this case, new investment streams
may be critical for any new development.
In view of the antecedents, this study will provide answer to the following research questions.
i.
What are the socio-economic characteristics of the maize farmers in the study
area?
ii.
What are the different maize extension technologies available and adopted by the
respondents in the study area?
iii.
Whatare the technical efficiency of the maize farmers in the study area?
iv.
What are the constraints faced by the respondents when adopting maize extension
technologies in the study area?
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Hypotheses of the study
H o 1: There is no significant relationship between the socio-economic characteristics of the
respondents and their maize output.
H o 2: Maize farmers are not technically efficient.
Materials and Method
The Study Area
This study was carried out in Oyo State, Nigeria. The state is predominantly agrarian.
It lies in the equatorial rainforest belt and the rainfall around this area varies from 1500mm
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to 1800mm per annum. There is distinct wet season from April to late October and dry
season from November to March; the areas have a mean annual temperature of 26.2
degree Celsius, the humidity is high between July and December and low between
December and February.
The main occupation of the people is farming and farms are semi commercial units,
which largely rely on rainfall as the chief source of water supply. In addition, farming
household structure is basically of two types; the nuclear type called farm family and the
extended type called the-farming household.
Majority of these farming households rely basically on the use of manual labour and
simple tools such as hoe, cutlasses and matchets, and make insignificant use of capital
intensive operation such as mechanization for ploughing, harrowing and ridging on their
individual farms. The principal stages of production in the farming cycle are land
preparation, planting, weeding and harvesting which require inputs of labour (man-days).
The shortage of family or household labour during the production season often
necessitates the introduction of hired labour to complement household labour on the farm.
This often forces the farm owner to obtain credit in order to pay wages as well as purchase
farm inputs (fertilizer, chemical and seed).
Source and Type of Data
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The data that were used for this study are mainly primary data, which were obtained through
the use of a structured questionnaire and interview schedule for the literate and non-literate
respondents.
A multi-stage stratified random sampling technique was adopted in this study. Oyo
state ADP has been stratified into four administrative zones (Ibadan/Ibarapa, Oyo, OkeOgun/Shaki and Ogbomoso). These zones were sub-divided into blocks and circles.
Comprehensive lists of farm families in all the zones were obtained from OYSADEP
Headquarters. Out of the total of 265,000 farm families listed under the ADP, Ibadan/Ibarapa
had 81,507, Oyo, 49,483, Oke-Ogun/Shaki 37,826 and Ogbomoso 96,229. To ensure
representativeness, two hundred and sixty copies of questionnaires were divided in proportion
to the number of circle in each zone.
The copies of questionnaire that were administered in each zone were further divided
in proportion to the number of farm families in each circle. Simple random sampling
technique was employed to select the farm families that were sampled in each circle.
Analytical Technique and Procedure
Descriptive Statistics
Descriptive statistics used includes the mean, standard deviation, frequency distribution and
coefficient of variation. They were used as tools to present the socio-economic and
demographic information of the respondents selected for the survey.
Inferential Statistics
Inferential Statistics such as Production function, Maximum Likelihood Estimates (MLE) were employed to
fit the stochastic frontier production function from which the technical efficiency indexes of
the farmers were derived,Battese and Coelli, (1995) and Yao and Liu (1998),while regression
analysis were also used to test the formulated hypotheses.
Model Specification ofStochastic Frontier Model
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With the aim of addressing the research questions raised in the introductory part of
this study and in light of the designed analytical framework, the appropriate model
specifications are made in the following two sections.
Empirical Stochastic Frontier Model
The choice of functional form in an empirical study is of prime importance, since the
functional form can significantly affect the results. In congruent with several works already
done in the area of technical efficiency and productivity measurement, such as Battese and
Coelli, (1995) and Yao and Liu (1998). Cobb-Douglas production function is fitted in this
study, because it doesn't impose general restrictions on the parameters nor on the technical
relationships among inputs and allows for easy measurement of the elasticity of factors of
production as well as the return to scale. The analyses involved two steps.
As a first stage in the productivity analysis, Ordinary Least Square (OLS) estimation was
made on the Cobb-Douglas production function. Based on the significance of the parameter
estimates, information was gained on which variables should be included in the stochastic
frontier analysis. In the production function five inputs of production, farm size (ha),
herbicide quantity (litre), seed quantity (Kg), labour (man-day), and fertilizer quantity (kg)
were included. The choice of the variables was made because these inputs are the
conventional inputs used in maize production in the study area.
The model of the stochastic frontier production for the estimation of the TE was specified as:
ln Yi = β 0 + β 1 ln X 1 + β 2 ln X 2 + β 3 ln X 3 + β 4 ln X 4 + β 5 ln X 5 + Vi − U i ............... 4)
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Where subscript i refers to the observation of the ith farmer, and
Y = Maize output (kg)
X 1 = Farm size under maize cultivation (ha)
X 2 = Herbicide quantity (litre)
X 3 = Seed quantity (Kg)
X 4 = labour (man-days)
X 5 = Fertilizer Quantity (kg)
β i 's = the parameters estimated
ln's = natural logarithms
V i = the two-sided, normally distributed random error
U i = the one-sided inefficiency component with a half-normal distribution.
The Inefficiency Model
In this study, the technical inefficiency was measured by the mode of the truncated
normal distribution (i.e. U i ) as a function of socio-economic factors (Yao and Liu, 1998).
Thus, the technical efficiency was simultaneously estimated. The determinant of the technical
efficiency was defined by:
U i = δ 0 + δ 1 Z 1 + δ 2 Z 2 + δ 3 Z 3 + δ 4 Z 4 + δ 5 Z 5 .................................... (5)
Where:
U i = technical inefficiency of the ith farmer
Z1 = Age of farmer (yrs)
Z2 = Family Size (number)
Z3 = Year of farming experience (yrs)
Z4 = Year of Education (yrs)
Z5 = Extension Contact (Yes = 1, No =0)
The above equation was used to examine the influence of some of the maize farmers’
socio-economic variables on their technical efficiency. Therefore, the socio-economic
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variables in equation above were included in the model to indicate their possible influence on
the technical efficiencies of the maize farmers.
In the presentation of estimates for the parameters of the above frontier production, two
basic models were considered. Model 1 is the traditional response function in which the
inefficiency effects (U i ) are not present. It is a special case of the stochastic frontier
production function model in which the parameter γ = 0. Model 2 is the general frontier
2
model where there is no restriction in which γ , σ s are present. The estimates of the
stochastic frontier production function were appraised using the generalized likelihood ratio
test, and the T-ratio for significant econometric relevance.
Technical Efficiency
The technical efficiency of production of the i-th farmer in the appropriate data set,
given the levels of his inputs, is defined by:
TE i = exp (−U i ) .........................................................................................(6)
From equations (3) and (4), the two components V i and U i are assumed to be independent of
2
each other, where V is the two-sided, normally distributed random error ( Vi ~ N (0, σ v ),
i
and U i is the one-sided inefficiency component with a half normal distribution (
2
U i ~ / N (0, σ u ) . Y and X are as defined earlier. The β’s are unknown parameters to be
i
i
estimated together with the variance parameters.
2
2
The variances of the parameters, symmetric V and one-sided U , are σ v and σ u
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i
i
2
respectively and the overall model variance given as σ are related thus:
2
2
σ 2 = σv + σu
.......................................................................... (7)
The measures of total variation of output from the frontier, which can be attributed to
technical efficiency, are lambda (λ) and gamma (γ) (Battese&Corra, 1977) while the
variability measures derived by Jondrowet al., (1982) are presented by equations (6) and (7):
λ=
σu
σv
……………………………….. ………………………. (8)
……………………………………………………..….
(9)
σu2
γ= 2
σ
On the assumption that V i and U i are independent and normally distributed, the parametersβ,
2
2
σ 2 , σ u , σ v , λ and γ were estimated by method of Maximum Likelihood Estimates
(MLE), using the computer program FRONTIER Version 4.1 (Coelli, 1996a). This computer
program also computed estimates of technical and allocative efficiencies.
The farm specific technical efficiency (TE) of the i-th farmer was estimated using the
expectation of U i conditional on the random variable (ε i ) as shown by Battese and Coelli
(1988). The TE of an individual farmer is defined in terms of the ratio of the observed output
to the corresponding frontier output given the available technology, that is:
Y
exp( X i β + Vi − U i )
TE i = i* =
= exp(−U i ) .................................. (10)
exp( X i β + Vi )
Yi
(Tadesse and Krishnamoorthy, 1997)
So that:
O ≤ TE ≤ 1
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RESULTS AND DISCUSSION
The section discuses the socio-economic characteristics of the respondents. The socio
economic characteristics examined are with special reference to age, gender, marital status,
educational level, family size and farm size etc.
Social economic characteristic of the respondent
Table 1:
Table 1 below showed that the mean ages of the respondents was 45.6 years. Most of
the respondents (60.3%)fallow within age range of 31 and 50 years. This indicates that most
of the respondents were still in their active age and this aids their maize production. It has
been shown that the farmers’ production and rate of adoption of maize extension technologies
decline with age after about 50 years (Kebede, 2001), which is supported by Khairo and
Battese(2005) who concluded that majority of farmers who adopted new extension
technologies were mainlyyouth.Majority(69.2%) of the respondents were male while 30.3%
of them were female. This implies that male were found to be more involved in maize
production than their female counterparts and the involvement of male in maize production
could be due to energy requirement of the farm enterprise in question.76% of the respondents
were married, 13% were widowed while only 10.1% of the respondents were single. The
results of marital status of the respondents suggest a high degree of level of responsibility and
a great capability for sound rational decision among the farmers. This is in conformity with
Landon et al, (1996) which concluded that marital status is a tutor, which is likely to
encourage the sustainability of adoption decisions. 35.7% of the respondents had no formal
education while 53.8% acquired primary school education, 7.7% also acquired secondary
school education, 2.7% also acquired tertiary school education. This may be due to the
individuals’ farmers’ background and this is expected to have some influence on their level of
receptivity to the extension training and services provided. This is in line with a study by
Roling and Pretty (1997), that farmers with more years of schooling tended to be more
technically sound than the farmers with no formal education. 35.7% of the respondents had
no formal education while 53.8% acquired primary school education, 7.7% also acquired
secondary school education, 2.7% also acquired tertiary school education. This may be due to
the individuals’ farmers’ background and this is expected to have some influence on their
level of receptivity to the extension training and services provided. This is in line with a study
by Roling and Pretty (1997), that farmers with more years of schooling tended to be more
technically sound than the farmers with no formal education.Agriculture in Nigeria is
characterized by substantial use of family labour. Table 9 revealed that the average family
size was found to be 5 members, only about 16.1% of the respondents had between 1 and 3
members in their family while 71.5% (majority) were having between 7 and above members
in their family. This is supported by Awudu and Richard (2001) that large families appeared
to be more efficient in adopting extension technologies than small families although larger
family members consumed more of farm produce than small family which usually reduce the
quantity of the produce made available for sale and the propensities to make more income.
Years of farming experience of a farmer contribute to his ability to manage his
holding efficiently through trial and error. Thus, the higher the experience of a farmer, the
higher the adoption rate will be. Table 10 showed that above 68.9% of the farmers have been
in farming for more than 10 years. The mean farming year is 21.26, it is therefore expected
that this groups of farmers will be able to manage their farm efficiently. Less than 30% have
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farming experience that is lesser than 10 years. This in line with the findings of Awudu and
Richard (2001) which concluded than farming experience contributes positively to
production.
Age Range (Years)
21-30
31-40
41-50
51-60
61-70
X = 47
Sex
Male
Female
Marital Status
Single
Widowed
Married
EducationalLevel
No formal Education
Primary Education
Secondary Education
Tertiary Education
Family Size
1-3
4-7
7-10
X =5
Years of Farming Experience
1-5
6-10
11-15
16-20
> 21
Total
X = 21.26, SD = 0.567.
Source: Field Survey, 2011.
Frequency
36
73
81
50
20
Percentage
13.8
28.1
31.2
19.2
7.7
180
80
69.2
30.8
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27
34
199
10.1
13
76
93
140
20
7
35.7
53.8
7.7
2.7
42
33
185
16.1
12.6
71.5
19
62
105
28
46
260
7.30
27.9
40.5
10.7
12.7
100.0
Table 2:
The knowledge of the farmer’s farm size is to give the idea of their scale of operation.
This helps to determine the number of work force to be employed given family labour force
hence the lost implication for farm operations which also determines farmer’s potential
income.
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Table 12 showed that 28.5% cultivated between 1 and 5 hectares while only 10% also
cultivated 20 hectares and above. Majority (53.8%) of the respondents cultivated between 6
and 10 hectares of land. The minimum farm size of the respondents in the study area is 1
hectare while the maximum is 22 hectares. A priori expectation showed that the greater the
land area of land cultivated, the higher will be the propensity to make more income.69.3%
claimed that they have contact with Extension Agents while 30.7% (minority) of the
respondents claimed that, they have no contact with the Extension Agents. the multiple
responses of the respondents on extension technologies adopted, 65.4% of them adopted
improved seed technology, 61.5% of the sampled respondents adopted spacing technology as
a means of boosting their maize production while 28.4%, 66.9% and 55.4% adopted thinning
maize, weed control and seed protection respectively. The table further revealed that 3.8%,
24.6% and 9.2% also adopted the use of chemical, fertilizer application and harvesting as
their own means of boosting maize production. Only 9.2% of the sampled respondents
adopted seed rate extension technology.the mean maize output is 2097kg with standard
deviation of 709 which indicate a large variability of output among the farmers. The
minimum maize output was 900kg while the maximum maize output was 4000kg. It further
shows that 19.2% of the respondents had maize output of between 1 and 1000kg, 26.5% had
been between 1001 and 2000 while 30.80%, 23.5% of the sample respondents had between
2001 and 3000kgs and 3001 – 4000kg of maize respectively. The variation in their output
implies that some maize farmers had access to some certain technology which aided their
maize production.
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Farm Size
Frequency
1-5
74
6-10
140
11-5
20
>20
26
Extension Contact
Contact
180
Non-contact
80
Extension Technologies Adopted
Improved seed
170
Spacing
160
Thinning
74
Weed control
174 66.9
Seed protection
144
Use of Chemical
40
Fertilizer application
10
Harvesting
64
Seed rate
24
*Multiple Responses
Maize Output (kg)
1-1000
50
1001-2000
69
2001-3000
89
3001-4000
61
Total
260
X =2092kg SD = 709
Source: Field Survey, 2011.
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Percentage
28.5
53.8
7.7
10.0
69.3
30.7
65.4
61.5
28.4
55.4
15.4
3.8
24.6
9.2
19.2
26.5
30.8
23.5
100.0
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Test of First Hypothesis
The hypothesis is stated in null form.
HO1: There is no significant relationship between the socio-economic characteristics of the
respondents and their maize output.
Table 22 shows that age, sex, years of education, farm size, religion, marital status, primary
occupation, other occupation, extension contact, years of farming experience and household
size were considered to be the socio economic variables. The result showed that age, years of
education were statistically significant at 10% level while sex, religion and farm size were
statistically significant at 5% level of significance. Years of education has the coefficient out
of the two socio-economic variable that is statistically significant at 10% with a value of
34.960 which implies that years of education is the most important variable that has a great
influence on the output of maize farmers, although all the statistically significant variable had
a significant influence on their output but years of education appear to be the most. Out of the
socio economicvariables that were significant at 5%, sex appear to be the most socioeconomic variable that has a great impact on the output of maize farmers in the study area.
Therefore the null hypothesis is hereby rejected and the alternative hypothesis is accepted i.e.
H A: There is significant relationship between the socio-economic characteristics of the
respondents and their maize output.
Regression Model Equation
Y = b0 + b 1 x 1 +b2 x2 +………..b nX n
Y = 1527.473+24.327+390.777+34.960+34.969 + 58.973 + 28.546 – 55.633
(681.323) (14.158) (168.163) (21.682) (106.398) (157.571) (63.122) (158.924)
(2.242) (1.718) (2.324) (1.614) (-0.329)(0.374) (0.452) (-0.350)
-5.642-67.813 – 254.85 - 22.422
(13.345) (26.419) (122.114) (57.035)
(-0.430) (-2.567) (-2.104) (-0.393)
Y = Maize output
X 1 = Age
X 2 = Sex
X 3 = years of education
X 4 = Marital status
X 5 = Primary occupation
X 6 = Other occupation
X 7 =Extension contact
X 8 = Years of farming experience
X 9 = Farm size
X 10 = Religion
X 11 = Household size
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Table 22: Parameter Estimates of the Regression Analysis of the Maize farmer in
Oyo State
Variable
Coefficient
Standard Error
T-value
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Constant
1527.473
681.323
2.242**
Age
24.327
14.158
1.718*
Sex
390.777
168.163
2.324**
Years of Education
34.960
21.682
1.674*
Marital Status
-34.969
106.3988
-0.329
Primary Occupation
58.973
157.571
0.374
Other Occupation
28.546
63.122
0.452
Extension Contact
-55.633
158.924
-0.350
Years of Farming Experience
-5.642
13.345
-0.430
Farm Size
-67.813
26.419
-2.567**
Religion
-254.851
121.114
-2.104**
Household Size
-22.422
57.035
-0.393
F
2.872
R2
0.622
Notes: *** = 1% level, ** = 5% * = 10%
Source: Computed from field survey, 2011
F-cal = 2.87 while F-tab = 2.34, therefore F-cal < F-tab, Ho is rejected hence alternative
hypothesis is accepted which says that there is significant relationship between the socioeconomic characteristics of the respondents and their maize output.
Therefore, the r2 shows that there is 62.2% variation in output of the respondents
which is accounted for by the independent variables (socio economic characteristics) while
the remaining 23.6% is caused by the exogenous variables (Error term).
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Teat of second hypothesis
The hypothesis states that maize farmers are not technically efficient. This section
discusses the results of technical efficiency of the maize farmers in Oyo-State. Two
functional forms of the stochastic production frontier model were fitted (Linear and Cobb
Douglas functional forms) but only the Cobb Douglas type provided the best fit based on the
explicit details of the technical efficiency of the maize farmers as well as the number of
significant variables in the model, more so Dawson and Lingard (1989) alluded to the fact
that the Cobb Douglas type has certain advantages over the other functional forms.
Sign and Significance of Estimates of Stochastic Frontier Production Function (i.e.
Cobb Douglas Frontier Function Type)
The coefficients of the variables are very important on discussing the resultsof the
analysis of data. These coefficients represent percentage changes in the dependent variables
as a result of percentage change in the independent variables.
Among the maize farmers in the study area, the variables that were significant
included labour (significant at 5%), fertilizer quantity used (significant at 1%), seed quantity
(significant at 1%), herbicides quantity used (significant at 1%) and adoption index of the
respondent (significant at 1%), while farm size are not significant at all known of
significance. By implication, the above finding revealed that the major productive inputs that
have great impact on the maize output among maize farmers were labour, fertilizer quantity
used, seed quantity used, herbicide quantity used and their adoption level towards extension
maize technologies. Herbicide quantity has the highest coefficient with avalue of 0.7746 in
the preferred model and by implication the herbicide quantity used existed as the most
important input that has a great impact on maize output of the maize farmers. However, for
every unit increase in herbicides quantity used there is less than proportionate increase in
maize output. It is also shown that farmers’ adoption level towards extension maize
technologies has a great influence on the technical efficiency of maize farmers in the study
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area. All the significant variables carried positive sign; the economic implication of the sign
is that any increases in the quantities of such variables would lead to an increase in maize
output of the maize farmers.
Negative coefficient of a variable might indicate an excessive utilization of such a
variable. In economic terms, any attempt to increase the quantities of significant variable will
be tantamount to raising the level of the maize output of maize farmers in the study area.
The estimated parameters of the inefficiency model in the stochastic frontier models
of the maize farmers in Oyo States presented in Table 23. The analysis of the inefficiency
model shown in the Table 23 showed that the signs and significance of the estimated
coefficients in the model have important policy implications on the technical efficiency of the
maize farmers.
Among the efficient variables only age is statistically significant at 5% level which
has negative coefficient (-0.2722). The above findings revealed that years of farming
expenditure and family size tend to increase the level of technical inefficiency of the maize
farmer while age of the farmers tend to reduce the level of technical inefficiency of the
respondents.
Technical Efficiency Analysis of Maize Farmers in Oyo State
The predicted technical efficiency estimates obtained using the estimated stochastic
frontier models for the individual maize farmers in the study is presented in Table 24.
It showed the predicted maize farmers specific technical efficiency (TE) indices
ranged from a minimum 42% to a maximum of 99% with a mean of 81%.
The mean technical efficiency of maize farmers (i.e. 81%) shows that maize farmers
weretechnical efficient, therefore the formulated hypothesis is rejected and alternative
hypothesis of accepted which started that maize farmers are technically efficient in the study
area.
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Maximum Likelihood Estimates for the parameter of the stochastic frontier production
function for maize farmers in Oyo State.
Variable
Parameter
Coefficient
T-value
Efficiency model
Constant
0.3205
48.7277
β0
Farm size
-0.1240
-02586
β1
Labour
0.1569
2.5346**
β2
Fertilizer quantity
0.1405
35579***
β3
Seed Quantity
0.1376
3.6881***
β4
Herbicides Quantity
0.7746
3.5774***
β5
Inefficiency Mode
Constant
Age
Years of Education
Years of farming Exp
Family size
Extension contact
Adoption Index
Variance Parameter
Sigma Squared
Gamma
Log likehood function
Copyright © 2013 SciResPub.
δ0
δ1
δ2
δ3
δ4
δ5
δ5
0.8502
-0.2722
-0.1710
0.8048
0.5723
-0.1044
0.2159
2.0754
-2.5303**
-1.4342
0.6918
1.1796
-09628
9.4741***
δ2
δ
0.1021
0.9999
67.1752
5.1675***
52.3087
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Notes: *** =1% level, ** =5%, * = 10%
Source: Computed from field survey, 2011.
Range of frequency distribution of Technical efficiencies of the maize farmers in Oyo
State
Efficiency score %
Technical Efficiency
Frequency
Percentage
0.40 – 0.60
72
27.7
0.61 – 0.80
68
26.2
0.81 – 1.0
120
46.1
Total
260
100.0
Mean %
0.8136
81%
Minimum %
0.4264
42%
Maximum %
0.9932
99%
Source:
Field Survey, 2011
Conclusions and Recommendation
Based on the findings of this study, the following conclusions were drawn.
The larger percentage of the sampled maize farmers were at their productive age,
male were more involved in maize production compared with female. Larger percent of
farmers adopted maize extension technologies. Extension contact appeared to have a serious
influenced on their adoption rate on maize extension technologies (Logit model). Maize
farmers were technically efficient in the study area (Mean technical efficiency of the
stochastic frontier).
The following recommendations are therefore suggested.
1.
There is need to create more awareness about the roles of extension service among the
farmers in the study area.
2.
The authorities concerned with organizing extension delivery should improve on the
frequency of extension contact in order to encourage farmers’ participation in
extension technologies when the need arises in the study area, and by extensionin the
state at large.
3.
The aforementioned recommendations can be combined with provision of inputs so as
to mobilize farmers to always participate in extension programme in the study area.
4.
More information about maize extension technologies should be disseminated through
mass media with adequate training instead of total reliance on the extension agent in
the dissemination of agricultural information.
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REFERENCES
Akinwumi J.A. (1970). Economics of Maize Production in Oyo Division, Western State,
Nigeria. Unpublished Msc Thesis, Department of Agricultural Economics,
University of Ibadanand Agriculture Organization of the United Nations.
IJOART
Antholt, C.H. (1994) ‘Getting ready for the twenty-first century: technical change and
institutional modernization in agriculture’. World Bank Technical Paper No.217.
Washington D.C.: World Bank.
Awudu. A and Richard E. (2001): “Technical Efficiency during Economic Reform in
Nicaragua:Evidence from Farm Household Survey Data.” Economic Systems, 25:
113-125.
Basnyat, B.B.(1995) Nepal's Agriculture, Sustainability and Intervention: Looking for New
Direction. Ph.DThesis ,Wageningen
Battese, G.E. and Coelli, T.J. (1988): “Production of Firm Level Technical Efficiencies with a
Generalized Frontier Production Function and Panel Delta”;Journal of Economics
38:387-389.
Battese, G.E. and Coelli, T.J. (1995): “A Model for Technical Inefficiency Effects in a Stochastic
Frontier Production Function for Panel Delta”. Empirical Economics 20:325- 335.
Battese, G.E. and Corra, G.S. (1977): “Estimation of a Production Function Model with
Application to the Pastoral Zone of Eastern Australia”; Australia Journal of Agricultural
Economics 21: 169-179.
Bravo-Ureta, B. E. and Pinheiro, A. E. (1997).“Technical, economic and allocative efficiency
in peasant farming: evidence from the Dominican Republic”. Developing Economies
35, 48-67.
Copyright © 2013 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue 12, December-2013
ISSN 2278-7763
73
Coelli, T.J. (1996a): “A Guide to Frontier Version 4.1 Computer Program for Stochastic Frontier
Function and Cost Function Estimation”. Unpublished Paper, Department of
Econometrics, University of New England, Armidale, NSW2351, Australia. Pp 32.
Ellis, F., 1988.Peasant Economics: Farm households and Agrarian Development. Cambridge
University Press, Cambridge, UK.
Falusi,
A.O. and O.A. Adubifa (1975).Economics of Fertilizer Distribution in
Nigeria.Technical Report FAM, Lagos.
Farrell, M.J., 1957. “The measurement of productive efficiency”. J. R. Stat. Soc., Ser. A,
120:253-290.
Hayami, Y. and Ruttan, V. W. (1985) Agricultural development: An international
perspective, Baltimore, USA, Johns Hopkins University Press.
Jondrow, J; Lovell, C.A.K.; Materov, I.S. Schmidt, P. (1982).“On The Estimation of Technical
Inefficiency in the Stochastic Frontier Production Function Model” Journal of
Econometrics 19: 233 – 238.
Kebede, T.A. ( 2001) . Farm Household Technical Efficiency : A stocastic frontier analysis A
study of Rice Producer in Mardi Watershed in the western 21 Development Region of
Nepal. A Master Thesis submitted to Department of Economics and Social Sciences,
Agriculture University of Norway.
IJOART
Khairo S.A. and G.E. Battese (2005):“A Study of Technical Inefficiencies of Maize Farmers
Within and outside the New Agricultural Extension Program in the Harari Region of
Ethiopia”S. Afr. J. Agric. Ext., 34:3
Landon, J. & Powell, R. M. (1996) A New approach to securing sustained growth in Kenya's
maize sector Wallingford, UK, CAB International.
Nelson, R.R. and Phelps O.P (2006).”Investment in Human, Technological Diffusion and
Economic Growth”.American Journal of Economic Review, 56:62-82.
Prinsley, R.; Dore, J.; Marks, Ni.;McGukian, N.; and Thompson, P. (1994) The role of
private sector in extension: A working paper. Rural Industries Research and
Development Corporation Occasional Paper No. 94/3. Australia.
Roling, N. (1995) ‘What to think of extension? A comparison of three models of extension
practice’. AERDD Bulletin. Reading, UK: University of Reading.
Roling, N., & Pretty, J. N. (1997).“Extension’s role in sustainable agricultural development.”
In: B. E. Swanson, R. P. Bentz, & A. J. Sofranko (eds.), Improving Agricultural
Extension: A reference manual, 181-191, FAO.
Sacay, O.J. (1987). An Agricultural Development Strategy for Nepal. Rome: Food
Copyright © 2013 SciResPub.
IJOART
International Journal of Advancements in Research & Technology, Volume 2, Issue 12, December-2013
ISSN 2278-7763
74
Shapiro K.H. and Muller, J. (2007).“Sources of Technical Efficiency: The Role of
Modernization and Information” Economic Development and Cultural Change,
25:293-310.
Stoneman, P. (2003). The Economic Analysis of Technological Change.Oxford University
Press.
Tweeten, L.G. (2007). “Theories Explaining the persistence of low Returns in a Growing
Farm Economy”. Journal of Farm Economics. pp 798-816.
Umali, D.L. and Schwartz, L. (1994) ‘Public and private agricultural extension:beyond
traditional frontiers’, World Bank Discussion Paper No. 236.Washington, D.C.:
World Bank
Yao, S. and Liu, Z. (1998): “Determinants of Grain Production and Technical Efficiency in
China”;Journal of Agricultural Economics 49 (2): 171 – 184.
IJOART
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